Acoustic configuration based on radio frequency sensing

The present disclosure generally relates to configuring acoustic settings of an electronic device. Aspects of the disclosure relate to systems and techniques for using radio frequency (RF) sensing to detect audio sources and/or configure acoustic settings.

Electronic devices are capable of providing sophisticated acoustic features such as voice and/or speech recognition. In some cases, users can recite audio instructions which can be used to control the electronic device. For example, users can speak to the electronic devices to initiate or control an application, such as playing music or requesting directions to a destination.

In order to implement various telecommunications functions, electronic devices can include hardware and software components that are configured to transmit and receive radio frequency (RF) signals. For example, a wireless device can be configured to communicate via Wi-Fi, 5G/New Radio (NR), Bluetooth™, and/or ultra-wideband (UWB), among others.

The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

Disclosed are systems, methods, apparatuses, and computer-readable media for configuring acoustic settings of an electronic device. According to at least one example, a method is provided for configuring acoustic settings of an electronic device. The method can include: obtaining a first set of radio frequency (RF) sensing data associated with a first plurality of received waveforms corresponding to a first transmitted waveform reflected off of a plurality of reflectors; determining a classification of a first reflector from the plurality of reflectors based on the first set of RF sensing data; and determining at least one acoustic setting based on the classification of the first reflector.

In another example, a wireless device is provided that includes at least one memory and at least one processor (e.g., configured in circuitry) coupled to the at least one memory. The at least one processor is configured to: obtain a first set of radio frequency (RF) sensing data associated with a first plurality of received waveforms corresponding to a first transmitted waveform reflected off of a plurality of reflectors; determine a classification of a first reflector from the plurality of reflectors based on the first set of RF sensing data; and determine at least one acoustic setting based on the classification of the first reflector.

In another example, a non-transitory computer-readable medium is provided that includes stored thereon at least one instruction that, when executed by one or more processors, cause the one or more processors to: obtain a first set of radio frequency (RF) sensing data associated with a first plurality of received waveforms corresponding to a first transmitted waveform reflected off of a plurality of reflectors; determine a classification of a first reflector from the plurality of reflectors based on the first set of RF sensing data; and determine at least one acoustic setting based on the classification of the first reflector.

In another example, an apparatus is provided. The apparatus includes: means for obtaining a first set of radio frequency (RF) sensing data associated with a first plurality of received waveforms corresponding to a first transmitted waveform reflected off of a plurality of reflectors; means for determining a classification of a first reflector from the plurality of reflectors based on the first set of RF sensing data; and means for determining at least one acoustic setting based on the classification of the first reflector.

In some aspects, the apparatus is or is part of a wireless device, such as mobile device (e.g., a mobile telephone or so-called “smart phone” or other mobile device), a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), an Internet-of-Things (IoT) device, a tablet, a personal computer, a laptop computer, a server computer, a wireless access point, a vehicle or component of a vehicle, or other any other device having an RF interface.

Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description.

Certain aspects and embodiments of this disclosure are provided below for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure. Some of the aspects and embodiments described herein may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive.

The ensuing description provides example embodiments, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the application as set forth in the appended claims.

Many electronic devices, such as smartphones, tablets, smart speakers, and laptops, are capable of detecting and processing audio signals. In some examples, an electronic device may continuously monitor for a keyword in an audio stream (e.g., the user's speech) that can be used to trigger additional device functionality. For instance, detection of a keyword can be used to enable a processor that is capable of interpreting audio commands from a user. A user can utilize verbal commands to control device functions such as increasing or decreasing volume, launching applications, sending text messages, initiating voice/video calls, etc.

Continuously monitoring for verbal commands from a user can result in higher power consumption and increased processing overhead. In the case of mobile electronic devices, the additional power consumption can result in reduced battery life. Furthermore, the continuous monitoring for verbal commands is often times unnecessary because the user is not in the vicinity of the electronic device.

In addition to the concerns related to high power consumption, proper processing of voice commands can be challenging when the electronic device is located in a noisy environment. For example, an electronic device may be located in a room with a television or radio that is producing sound that may interfere with voice commands from a user. While there are sound filtering algorithms that can be implemented to help alleviate this problem, the effectiveness of such algorithms can be limited by an inability to properly identify and locate the source of the sound interference.

It would be desirable to develop a technique that would permit a device to identify sound sources in order to configure acoustic settings for implementing speech detection and processing. Moreover, it would be desirable to develop a technique that reduces latency in speech recognition while also improving power management to reduce overall power consumption and conserve battery life. Furthermore, it would be desirable to leverage existing radio frequency (RF) interfaces on devices to perform these techniques.

Systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively referred to as “systems and techniques”) are described herein for configuring acoustic settings of an electronic device. The systems and techniques provide the ability for an electronic device to collect RF sensing data that can be used to locate and identify reflectors (e.g., humans, static or dynamic objects, structural elements, etc.) in the surrounding environment. In some aspects, the RF sensing data can be used to develop an acoustic map of the surrounding environment and to identify reflectors that may generate sound and/or affect the propagation of sound waves in the environment. In some examples, the RF sensing data can be used to detect motion (e.g., user movement), determine the presence of a user, determine the orientation of the user's face, identify a user, and/or perform facial authentication.

In some aspects, the RF sensing data can be collected by utilizing wireless interfaces that are capable of simultaneously performing transmit and receive functions (e.g., a monostatic configuration). In other aspects, the RF sensing data can be collected by utilizing a bistatic configuration in which the transmit and receive functions are performed by different devices (e.g., a first wireless device transmits an RF waveform and a second wireless device receives the RF waveform and any corresponding reflections). Examples will be described herein using Wi-Fi as an illustrative example. However, the systems and techniques are not limited to Wi-Fi. For example, in some cases, the systems and techniques can be implemented using 5G/New Radio (NR), such as using millimeter wave (mmWave) technology. In some cases, the systems and techniques can be implemented using other wireless technologies, such as Bluetooth™, ultra-wideband (UWB), among others.

In some examples, a device can include a Wi-Fi interface that is configured to implement algorithms having varying levels of RF sensing resolution based upon a bandwidth of a transmitted RF signal, a number of spatial streams, a number of antennas configured to transmit an RF signal, a number of antennas configured to receive an RF signal, a number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive an RF signal), a sampling rate, or any combination thereof. For example, the Wi-Fi interface of the device can be configured to implement a low-resolution RF sensing algorithm that consumes a small amount of power and can operate in the background when the device is in a “locked” state and/or in a “sleep” mode. In some instances, the low-resolution RF sensing algorithm can be used by the device as a coarse detection mechanism that can sense motion within a certain proximity of the device. In certain aspects, the low-resolution RF sensing algorithm can be used as a trigger to configure acoustic settings on the device (e.g., enable speech detection, configure a spatial sound filter, etc.). In some aspects, the detection of motion by using the low-resolution RF sensing algorithm can trigger the device to perform a higher resolution RF sensing algorithm (e.g., a mid-resolution RF sensing algorithm, a high-resolution RF sensing algorithm, or other higher resolution RF sensing algorithm, as discussed herein) prior to configuring one or more acoustic settings.

In some examples, the device's Wi-Fi interface can be configured to implement a mid-resolution RF sensing algorithm. The transmitted RF signal that is utilized for the mid-resolution RF sensing algorithm can differ from the low-resolution RF sensing algorithm by having a higher bandwidth, a higher number of spatial streams, a higher number of spatial links (e.g., a higher number of antennas configured to receive an RF signal and/or a higher number of spatial streams), a higher sampling rate (corresponding to a smaller sampling interval), or any combination thereof. In some instances, the mid-resolution RF sensing algorithm can be used to detect the presence of a user (e.g., detect head or other body part, such as face, eyes, etc.) as well as motion that is in the device's proximity. In some examples, the mid-resolution RF sensing algorithm can be invoked in response to detecting motion in the proximity of the device by using the low-resolution RF sensing algorithm, as noted above. In certain examples, the mid-resolution RF sensing algorithm can also be used as a trigger to configure acoustic settings on the device (e.g., enable speech detection, configure a spatial sound filter, etc.). In some cases, detecting the presence of the user by using the mid-resolution RF sensing algorithm can trigger the device to perform a higher resolution RF sensing algorithm (e.g., a high-resolution RF sensing algorithm or other higher resolution RF sensing algorithm, as discussed herein) prior to configuring one or more acoustic settings.

In another example, the device's Wi-Fi interface can be configured to implement a high-resolution RF sensing algorithm. The transmitted RF signal that is utilized for the high-resolution RF sensing algorithm can differ from the mid-resolution RF sensing algorithm and the low-resolution RF sensing algorithm by having a higher bandwidth, a higher number of spatial streams, a higher number of spatial links (e.g., a higher number of antennas configured to receive an RF signal and/or a higher number of spatial streams), a higher sampling rate, or any combination thereof. In some instances, the high-resolution RF sensing algorithm can be used to identify a user, to detect the presence of a user, and/or to detect motion in the proximity of the device. In some examples, the high-resolution RF sensing algorithm can be invoked in response to detecting motion in the proximity of the device and/or in response to detecting the presence of the user. In certain cases, the high-resolution RF sensing algorithm can be used as a trigger to configure acoustic settings on the device (e.g., enable speech detection, configure a spatial sound filter, etc.).

In some examples, the systems and techniques can perform RF sensing associated with each of the aforementioned algorithms by implementing a device's Wi-Fi interface having at least two antennas that can be used to simultaneously transmit and receive an RF signal. In some instances, the antennas can be omnidirectional such that RF signals can be received from and transmitted in all directions. For example, a device may utilize a transmitter of its Wi-Fi interface to transmit an RF signal and simultaneously enable a Wi-Fi receiver of the Wi-Fi interface so that the device may receive any reflected signals (e.g., from reflectors such as objects or humans). The Wi-Fi receiver can also be configured to detect leakage signals that are transferred from the Wi-Fi transmitter's antenna to the Wi-Fi receiver's antenna without reflecting from any objects. In doing so, the device may gather RF sensing data in the form of channel state information (CSI) data relating to the direct paths (leakage signals) of the transmitted signal together with data relating to the reflected paths of the signals received that correspond to the transmitted signal.

In some aspects, the CSI data can be used to calculate the distance of the reflected signals as well as the angle of arrival. The distance and angle of the reflected signals can be used to detect motion, determine the presence of a user (e.g., detect face, eyes, feet, hands, etc.), and/or identify the user as discussed above. In some examples, the distance of the reflected signals and the angle of arrival can be determined using signal processing, machine learning algorithms, using any other suitable technique, or any combination thereof. In one example, the distance of the reflected signals can be calculated by measuring the difference in time from reception of the leakage signal to the reception of the reflected signals. In another example, the angle of arrival can be calculated by utilizing an antenna array to receive the reflected signals and measuring the difference in received phase at each element of the antenna array. In some instances, the distance of the reflected signals together with the angle of arrival of the reflected signals can be used to identify presence and orientation characteristics of a user.

In some examples, one or more of the various RF sensing algorithms discussed herein can be used to locate and identify sound sources in the surrounding environment. In some aspects, the RF sensing data can be used to determine the position of the sound sources in order to configure one or more spatial sound filters. In one example, a fixed beamformer can be directed towards a targeted sound source such as a human that is identified using RF sensing techniques. In another example, null beamforming can be directed towards acoustic interfering objects identified using RF sensing techniques (e.g., television, radio, pets, appliances, other humans, etc.).

In some aspects, one or more of the various RF sensing algorithms discussed herein can be used together with acoustic sensing algorithms to locate and identify sound sources in the surrounding environment. In one example, RF sensing data can be used to determine the location and shape of an object, which can be correlated with acoustic sensing data to identify and/or classify the object. In another example, RF sensing data can be used to determine a user identity (e.g., based on shape or characteristics of user body or body parts) that can be correlated with acoustic sensing data corresponding to the user (e.g., voice signature). In some examples, RF sensing data and/or acoustic sensing data can be processed using artificial intelligence and/or machine learning algorithms.

Various aspects of the systems and techniques described herein will be discussed below with respect to the figures.illustrates an example of a computing systemof an Internet-of-Things (IoT) device. The IoT deviceis an example of a device that can include hardware and software for the purpose of connecting and exchanging data with other devices and systems using computer networks (e.g., the internet). For example, the IoT devicecan include a virtual assistant, smart speaker, smart appliance, mobile phone, router, tablet computer, laptop computer, tracking device, wearable device (e.g., a smart watch, glasses, an XR device, etc.), a vehicle (or a computing device of a vehicle), and/or another device used by a user to communicate over a wireless communications network. In some cases, the device can be referred to as a station (STA), such as when referring to a device configured to communicate using the Wi-Fi standard. In some cases, the device can be referred to as user equipment (UE), such as when referring to a device configured to communicate using 5G/New Radio (NR), Long-Term Evolution (LTE), or other telecommunication standard.

The computing systemincludes software and hardware components that can be electrically or communicatively coupled via a bus(or may otherwise be in communication, as appropriate). For example, the computing systemincludes one or more processors. The one or more processorscan include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, dedicated hardware, any combination thereof, and/or other processing device/s and/or system/s. The buscan be used by the one or more processorsto communicate between cores and/or with the one or more memory devices.

The computing systemmay also include one or more memory devices, one or more digital signal processors (DSPs), one or more subscriber identity modules (SIMs), one or more modems, one or more wireless transceivers, one or more antennas, one or more input devices(e.g., a camera, a mouse, a keyboard, a touch sensitive screen, a touch pad, a keypad, a microphone or a microphone array, and/or the like), and one or more output devices(e.g., a display, a speaker, a printer, and/or the like).

The one or more wireless transceiverscan receive wireless signals (e.g., signal) via antennafrom one or more other devices, such as other user devices, network devices (e.g., base stations such as eNBs and/or gNBs, WiFi access points (APs) such as routers, range extenders or the like, etc.), cloud networks, and/or the like. In some examples, the computing systemcan include multiple antennas or an antenna array that can facilitate simultaneous transmit and receive functionality. Antennacan be an omnidirectional antenna such that RF signals can be received from and transmitted in all directions. The wireless signalmay be transmitted via a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), wireless local area network (e.g., a WiFi network), a Bluetooth™ network, and/or other network. In some examples, the one or more wireless transceiversmay include an RF front end including one or more components, such as an amplifier, a mixer (also referred to as a signal multiplier) for signal down conversion, a frequency synthesizer (also referred to as an oscillator) that provides signals to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, among other components. The RF front-end can generally handle selection and conversion of the wireless signalsinto a baseband or intermediate frequency and can convert the RF signals to the digital domain.

In some cases, the computing systemcan include a coding-decoding device (or CODEC) configured to encode and/or decode data transmitted and/or received using the one or more wireless transceivers. In some cases, the computing systemcan include an encryption-decryption device or component configured to encrypt and/or decrypt data (e.g., according to the Advanced Encryption Standard (AES) and/or Data Encryption Standard (DES) standard) transmitted and/or received by the one or more wireless transceivers.

The one or more SIMscan each securely store an international mobile subscriber identity (IMSI) number and related key assigned to the user of the IoT device. The IMSI and key can be used to identify and authenticate the subscriber when accessing a network provided by a network service provider or operator associated with the one or more SIMs. The one or more modemscan modulate one or more signals to encode information for transmission using the one or more wireless transceivers. The one or more modemscan also demodulate signals received by the one or more wireless transceiversin order to decode the transmitted information. In some examples, the one or more modemscan include a WiFi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and/or other types of modems. The one or more modemsand the one or more wireless transceiverscan be used for communicating data for the one or more SIMs.

The computing systemcan also include (and/or be in communication with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices), which can include, without limitation, local and/or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device such as a RAM and/or a ROM, which can be programmable, flash-updateable and/or the like. Such storage devices may be configured to implement any appropriate data storage, including without limitation, various file systems, database structures, and/or the like.

In various embodiments, functions may be stored as one or more computer-program products (e.g., instructions or code) in memory device(s)and executed by the one or more processor(s)and/or the one or more DSPs. The computing systemcan also include software elements (e.g., located within the one or more memory devices), including, for example, an operating system, device drivers, executable libraries, and/or other code, such as one or more application programs, which may comprise computer programs implementing the functions provided by various embodiments, and/or may be designed to implement methods and/or configure systems, as described herein.

In some aspects, the IoT devicecan include means for performing operations described herein. The means can include one or more of the components of the computing system. For example, the means for performing operations described herein may include one or more of input device(s), SIM(s), modems(s), wireless transceiver(s), output device(s), DSP(s), processors, memory device(s), and/or antenna(s).

In some aspects, IoT devicecan include: means for obtaining a first set of radio frequency (RF) sensing data associated with a first plurality of received waveforms corresponding to a first transmitted waveform reflected off of a plurality of reflectors; means for determining a classification of a first reflector from the plurality of reflectors based on the first set of RF sensing data; and means for determining at least one acoustic setting based on the classification of the first reflector. In some examples, the means for obtaining can include the one or more wireless transceivers, the one or more modems, the one or more SIMs, the one or more processors, the one or more DSPs, the one or more memory devices, any combination thereof, or other component(s) of the wireless device. In some examples, the means for determining can include the one or more processors, the one or more DSPs, the one or more memory devices, any combination thereof, or other component(s) of the wireless device.

is a diagram illustrating an example of a wireless devicethat utilizes RF sensing techniques to perform one or more functions, such as locating and identifying a reflector. In some aspects, reflectorcan correspond to a human user and the RF sensing techniques can be used to detect motion of a user, detect presence of a user, identify a user, detect orientation characteristics of a user, any combination thereof, and/or perform other functions. In some examples, the wireless devicecan be the IoT device, such as a mobile phone, a tablet computer, a wearable device, or other device that includes at least one RF interface. In some examples, the wireless devicecan be a device that provides connectivity for a user device (e.g., for IoT device), such as a wireless access point (AP), a base station (e.g., a gNB, eNB, etc.), or other device that includes at least one RF interface.

In some aspects, wireless devicecan include one or more components for transmitting an RF signal. Wireless devicecan include a digital-to-analog converter (DAC)that is capable of receiving a digital signal or waveform (e.g., from a microprocessor, not illustrated) and converting the signal or waveform to an analog waveform. The analog signal that is the output of DACcan be provided to RF transmitter. The RF transmittercan be a Wi-Fi transmitter, a 5G/NR transmitter, a Bluetooth™ transmitter, or any other transmitter capable of transmitting an RF signal.

RF transmittercan be coupled to one or more transmitting antennas such as TX antenna. In some examples, TX antennacan be an omnidirectional antenna that is capable of transmitting an RF signal in all directions. For example, TX antennacan be an omnidirectional Wi-Fi antenna that can radiate Wi-Fi signals (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.) in a 360-degree radiation pattern. In another example, TX antennacan be a directional antenna that transmits an RF signal in a particular direction.

In some examples, wireless devicecan also include one or more components for receiving an RF signal. For example, the receiver lineup in wireless devicecan include one or more receiving antennas such as RX antenna. In some examples, RX antennacan be an omnidirectional antenna capable of receiving RF signals from multiple directions. In other examples, RX antennacan be a directional antenna that is configured to receive signals from a particular direction. In further examples, both TX antennaand RX antennacan include multiple antennas (e.g., elements) configured as an antenna array.

Wireless devicecan also include an RF receiverthat is coupled to RX antenna. RF receivercan include one or more hardware components for receiving an RF waveform such as a Wi-Fi signal, a Bluetooth™ signal, a 5G/NR signal, or any other RF signal. The output of RF receivercan be coupled to an analog-to-digital converter (ADC). ADCcan be configured to convert the received analog RF waveform into a digital waveform that can be provided to a processor such as a digital signal processor (not illustrated).

In one example, wireless devicecan implement RF sensing techniques by causing TX waveformto be transmitted from TX antenna. Although TX waveformis illustrated as a single line, in some cases, TX waveformcan be transmitted in all directions by an omnidirectional TX antenna. In one example, TX waveformcan be a Wi-Fi waveform that is transmitted by a Wi-Fi transmitter in wireless device. In some cases, TX waveformcan correspond to a Wi-Fi waveform that is transmitted at or near the same time as a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some examples, TX waveformcan be transmitted using the same or a similar frequency resource as a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some aspects, TX waveformcan correspond to a Wi-Fi waveform that is transmitted separately from a Wi-Fi data communication signal and/or a Wi-Fi control signal (e.g., TX waveformcan be transmitted at different times and/or using a different frequency resource).

In some examples, TX waveformcan correspond to a 5G NR waveform that is transmitted at or near the same time as a 5G NR data communication signal or a 5G NR control function signal. In some examples, TX waveformcan be transmitted using the same or a similar frequency resource as a 5G NR data communication signal or a 5G NR control function signal. In some aspects, TX waveformcan correspond to a 5G NR waveform that is transmitted separately from a 5G NR data communication signal and/or a 5G NR control signal (e.g., TX waveformcan be transmitted at different times and/or using a different frequency resource).

In some aspects, one or more parameters associated with TX waveformcan be modified that may be used to increase or decrease RF sensing resolution. The parameters may include frequency, bandwidth, number of spatial streams, the number of antennas configured to transmit TX waveform, the number of antennas configured to receive a reflected RF signal corresponding to TX waveform, the number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive an RF signal), the sampling rate, or any combination thereof.

In further examples, TX waveformcan be implemented to have a sequence that has perfect or almost perfect autocorrelation properties. For instance, TX waveformcan include single carrier Zadoff sequences or can include symbols that are similar to orthogonal frequency-division multiplexing (OFDM) Long Training Field (LTF) symbols. In some cases, TX waveformcan include a chirp signal, as used, for example, in a Frequency-Modulated Continuous-Wave (FM-CW) radar system. In some configurations, the chirp signal can include a signal in which the signal frequency increases and/or decreases periodically in a linear and/or an exponential manner.

In some aspects, wireless devicecan further implement RF sensing techniques by performing concurrent transmit and receive functions. For example, wireless devicecan enable its RF receiverto receive at or near the same time as it enables RF transmitterto transmit TX waveform. In some examples, transmission of a sequence or pattern that is included in TX waveformcan be repeated continuously such that the sequence is transmitted a certain number of times or for a certain duration of time. In some examples, repeating a pattern in the transmission of TX waveformcan be used to avoid missing the reception of any reflected signals if RF receiveris enabled after RF transmitter. In one example implementation, TX waveformcan include a sequence having a sequence length L that is transmitted two or more times, which can allow RF receiverto be enabled at a time less than or equal to L in order to receive reflections corresponding to the entire sequence without missing any information.

By implementing simultaneous transmit and receive functionality, wireless devicecan receive any signals that correspond to TX waveform. For example, wireless devicecan receive signals that are reflected from objects or people that are within range of TX waveform, such as RX waveformreflected from reflector. Wireless devicecan also receive leakage signals (e.g., TX leakage signal) that are coupled directly from TX antennato RX antennawithout reflecting from any objects. For example, leakage signals can include signals that are transferred from a transmitter antenna (e.g., TX antenna) on a wireless device to a receive antenna (e.g., RX antenna) on the wireless device without reflecting from any objects. In some cases, RX waveformcan include multiple sequences that correspond to multiple copies of a sequence that are included in TX waveform. In some examples, wireless devicecan combine the multiple sequences that are received by RF receiverto improve the signal to noise ratio (SNR).

Wireless devicecan further implement RF sensing techniques by obtaining RF sensing data associated with each of the received signals corresponding to TX waveform. In some examples, the RF sensing data can include channel state information (CSI) data relating to the direct paths (e.g., leakage signal) of TX waveformtogether with data relating to the reflected paths (e.g., RX waveform) that correspond to TX waveform.

In some aspects, RF sensing data (e.g., CSI data) can include information that can be used to determine the manner in which an RF signal (e.g., TX waveform) propagates from RF transmitterto RF receiver. RF sensing data can include data that corresponds to the effects on the transmitted RF signal due to scattering, fading, and/or power decay with distance, or any combination thereof. In some examples, RF sensing data can include imaginary data and real data (e.g., I/Q components) corresponding to each tone in the frequency domain over a particular bandwidth.

In some examples, RF sensing data can be used to calculate distances and angles of arrival that correspond to reflected waveforms, such as RX waveform. In further examples, RF sensing data can also be used to detect motion, determine location, detect changes in location or motion patterns, obtain channel estimation, or any combination thereof. In some cases, the distance and angle of arrival of the reflected signals can be used to identify the size, position, movement, or orientation of users in the surrounding environment (e.g., reflector) in order to detect user presence/proximity, detect user attention, and/or identify a user/object.

Wireless devicecan calculate distances and angles of arrival corresponding to reflected waveforms (e.g., the distance and angle of arrival corresponding to RX waveform) by utilizing signal processing, machine learning algorithms, using any other suitable technique, or any combination thereof. In other examples, wireless devicecan send the RF sensing data to another computing device, such as a server, that can perform the calculations to obtain the distance and angle of arrival corresponding to RX waveformor other reflected waveforms.